---
title: "COVID-19 Explorer"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
# Install devtools version since it is updated daily
#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)

# Load packages
library(flexdashboard)
library(tidyverse)
library(wppExplorer)
library(plotly)
library(tsibble)
library(fable)
library(feasts)

# Set color profiles
confirmed_color <- "forestgreen"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"

# Collect USA details by City and County
df_details <- read.csv("https://coronadatascraper.com/timeseries.csv",
                       stringsAsFactors = FALSE)

df_details_country <- df_details %>% 
  filter(level == "country") %>% 
  select(-name, -level) %>% 
  mutate(date = lubridate::ymd(date)) %>% 
  group_by(country) %>% 
  filter(date == last(date)) %>% 
  mutate_at(vars(cases:tested), replace_na, 0)

df_details_USA_tested <- df_details_country %>% 
  filter(country == "United States")

data(iso3166)

iso3166 <- iso3166 %>% 
  mutate(name = as.character(name),
         a2 = as.character(charcode),
         a3 = as.character(charcode3))

COVID_cumsum <- df_details %>% 
  filter(level == "country") %>% 
  select(-name, -level, -city, -county, -state) %>% 
  mutate_at(vars(cases:tested), replace_na, 0) %>% 
  mutate(date = lubridate::ymd(date)) %>% 
  rename(Country = country,
         Date = date,
         Confirmed = cases,
         Recovered = recovered,
         Death = deaths,
         Tested = tested)

COVID_cumsum_USA_by_County <- df_details %>% 
  filter(level == "county",
         aggregate == "county",
         country == "United States") %>% 
  select(-name, -level, -city) %>% 
  mutate_at(vars(cases:tested), replace_na, 0) %>% 
  rename(County = county,
         State = state,
         Country = country,
         Date = date,
         Confirmed = cases,
         Recovered = recovered,
         Death = deaths,
         Tested = tested) %>% 
  mutate(Date = lubridate::ymd(Date)) %>% 
  select(Country, State, County, everything())

COVID_cumsum_USA_by_State <- df_details %>% 
  filter(level == "state",
         aggregate == "county",
         country == "United States") %>% 
  select(-name, -level, -country, -city, -county) %>% 
  mutate_at(vars(cases:tested), replace_na, 0) %>% 
  rename(State = state,
         Date = date,
         Confirmed = cases,
         Recovered = recovered,
         Death = deaths,
         Tested = tested) %>% 
  mutate(Date = lubridate::ymd(Date)) %>% 
  select(State, everything())

COVID_cumsum_USA_by_County_map <- COVID_cumsum_USA_by_County %>% 
  filter(Date == last(Date)) %>% 
  mutate(Percent_of_County_Infected = Confirmed / population) %>% 
  arrange(Percent_of_County_Infected)

COVID_cumsum_confirmed <- COVID_cumsum %>% 
  group_by(Country) %>% 
  filter(Date == last(Date)) %>%  
  ungroup() %>% 
  select(Country, Date, Confirmed) %>% 
  arrange(-Confirmed)

COVID_cumsum_death <- COVID_cumsum %>% 
  group_by(Country) %>% 
  filter(Date == last(Date)) %>%  
  ungroup() %>%
  select(Country, Date, Death) %>% 
  arrange(-Death)

COVID_Date_of_100 <- COVID_cumsum %>% 
  arrange(Country, Date) %>% 
  group_by(Country) %>% 
  filter(Confirmed >= 100) %>% 
  mutate(Date_100_Surpassed = first(Date)) %>% 
  filter(Confirmed == first(Confirmed)) %>% 
  ungroup() %>% 
  rename(Cases_When_100_Surpassed = Confirmed) %>% 
  distinct(Country, Date_100_Surpassed, Cases_When_100_Surpassed)

COVID_totals <- COVID_cumsum %>% 
  group_by(Country) %>% 
  filter(Date == last(Date)) %>%  
  ungroup() %>%
  select(Country, Date, Confirmed, Death, population) %>% 
  left_join(COVID_Date_of_100, by = c("Country" = "Country")) %>% 
  mutate(Days_Since_100_Surpassed = as.integer(difftime(last(Date), Date_100_Surpassed, units = "days")),
         Infected_per_Day_since_100 = round((Confirmed - Cases_When_100_Surpassed)/Days_Since_100_Surpassed, 0)) %>% 
  arrange(-Infected_per_Day_since_100) %>% 
  mutate(Percent_Pop_Confirmed = Confirmed/population,
         Death_Rate = Death/Confirmed)

COVID_cumsum_plotly_confirmed <- COVID_cumsum %>% 
  filter(Country %in% head(COVID_totals$Country, n = 10))

COVID_series_confirmed <- COVID_cumsum %>% 
  distinct(Country, Date, Confirmed) %>% 
  filter(Country %in% head(COVID_totals$Country, n = 10)) %>%
  as_tsibble(index = Date, key = Country) %>% 
  fill_gaps(.full = TRUE)

COVID_series_death <- COVID_cumsum %>% 
  distinct(Country, Date, Death) %>% 
  filter(Country %in% head(COVID_totals$Country, n = 10)) %>%
  as_tsibble(index = Date, key = Country) %>% 
  fill_gaps(.full = TRUE)

COVID_series_confirmed_fit <- COVID_series_confirmed %>% 
  model(arima = ARIMA(Confirmed, stepwise = FALSE, approximation = FALSE)) #, stepwise = FALSE, approximation = FALSE

COVID_series_death_fit <- COVID_series_death %>% 
  model(arima = ARIMA(Death, stepwise = FALSE, approximation = FALSE)) #, stepwise = FALSE, approximation = FALSE

COVID_series_confirmed_fcast <- COVID_series_confirmed_fit %>% 
  forecast(h = "4 weeks")

COVID_series_death_fcast <- COVID_series_death_fit %>% 
  forecast(h = "4 weeks")

COVID_confirmed_fcast_df <- COVID_series_confirmed_fcast %>% 
  as_tsibble() %>% 
  select(Country, Date, Values = Confirmed) %>% 
  lapply(unlist) %>% 
  as_tibble() %>% 
  mutate(Values = round(Values, digits = 0))

COVID_death_fcast_df <- COVID_series_death_fcast %>% 
  as_tsibble() %>% 
  select(Country, Date, Values = Death) %>% 
  lapply(unlist) %>% 
  as_tibble() %>% 
  mutate(Values = round(Values, digits = 0))

# Join actuals and forecast
temp_COVID_cumsum_confirmed_hist <- COVID_cumsum %>% 
  select(Country, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

temp_COVID_cumsum_death_hist <- COVID_cumsum %>% 
  select(Country, Date, Values = Death) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_confirmed_hist_and_pred <- 
  rbind(temp_COVID_cumsum_confirmed_hist %>%
          filter(Country %in% head(COVID_totals$Country, n = 10)),
        COVID_confirmed_fcast_df)

COVID_cumsum_death_hist_and_pred <- 
  rbind(temp_COVID_cumsum_death_hist %>%
          filter(Country %in% head(COVID_totals$Country, n = 10)),
        COVID_death_fcast_df)

# Replicate time series ARIMA forecast by State, missing implicit dates as needed
COVID_series_USA_confirmed <- COVID_cumsum_USA_by_State %>% 
  select(State, Date, Confirmed) %>% 
  as_tsibble(index = Date, key = State) %>% 
  fill_gaps(.full = TRUE)

COVID_series_USA_death <- COVID_cumsum_USA_by_State %>% 
  select(State, Date, Death) %>% 
  as_tsibble(index = Date, key = State) %>% 
  fill_gaps(.full = TRUE)

COVID_series_USA_confirmed_fit <- COVID_series_USA_confirmed %>% 
  model(arima = ARIMA(Confirmed, stepwise = FALSE, approximation = FALSE)) #, stepwise = FALSE, approximation = FALSE

COVID_series_USA_death_fit <- COVID_series_USA_death %>% 
  model(arima = ARIMA(Death, stepwise = FALSE, approximation = FALSE)) #, stepwise = FALSE, approximation = FALSE

COVID_series_USA_confirmed_fcast <- COVID_series_USA_confirmed_fit %>% 
  forecast(h = "4 weeks")

COVID_series_USA_death_fcast <- COVID_series_USA_death_fit %>% 
  forecast(h = "4 weeks")

COVID_USA_confirmed_fcast_df <- COVID_series_USA_confirmed_fcast %>% 
  as_tsibble() %>% 
  select(State, Date, Values = Confirmed) %>% 
  lapply(unlist) %>% 
  as_tibble() %>% 
  mutate(Values = round(Values, digits = 0))

COVID_USA_death_fcast_df <- COVID_series_USA_death_fcast %>% 
  as_tsibble() %>% 
  select(State, Date, Values = Death) %>% 
  lapply(unlist) %>% 
  as_tibble() %>% 
  mutate(Values = round(Values, digits = 0))

# Join actuals and forecast
temp_COVID_cumsum_USA_confirmed_hist <- COVID_cumsum_USA_by_State %>% 
  select(State, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

temp_COVID_cumsum_USA_death_hist <- COVID_cumsum_USA_by_State %>% 
  select(State, Date, Values = Death) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_USA_confirmed_hist_and_pred <- 
  rbind(temp_COVID_cumsum_USA_confirmed_hist,
        COVID_USA_confirmed_fcast_df)

COVID_cumsum_USA_death_hist_and_pred <- 
  rbind(temp_COVID_cumsum_USA_confirmed_hist,
        COVID_USA_death_fcast_df)

# Rolling averages
COVID_cumsum_plotly_confirmed_3dayMA <- COVID_cumsum_plotly_confirmed %>% 
  group_by(Country) %>% 
  mutate(Confirmed_Lag = lag(Confirmed, n = 3),
         Rolling_Avg_3_day = round((Confirmed - Confirmed_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_USA_confirmed_hist_3dayMA <- temp_COVID_cumsum_USA_confirmed_hist %>% 
  group_by(State) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_California_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "California") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_California_by_county_3dayMA <- COVID_cumsum_California_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_GA_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "Georgia") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_GA_by_county_3dayMA <- COVID_cumsum_GA_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_IL_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "Illinois") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_IL_by_county_3dayMA <- COVID_cumsum_IL_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_FL_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "Florida") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_FL_by_county_3dayMA <- COVID_cumsum_FL_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_NY_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "New York") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_NY_by_county_3dayMA <- COVID_cumsum_NY_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_DC_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "Washington, D.C.") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_DC_by_county_3dayMA <- COVID_cumsum_DC_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_CO_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "Colorado") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_CO_by_county_3dayMA <- COVID_cumsum_CO_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_TX_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "Texas") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_TX_by_county_3dayMA <- COVID_cumsum_TX_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_OH_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "Ohio") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_OH_by_county_3dayMA <- COVID_cumsum_OH_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))

COVID_cumsum_SC_by_county <- COVID_cumsum_USA_by_County %>% 
  filter(State == "South Carolina") %>% 
  select(County, Date, Values = Confirmed) %>% 
  mutate(Values = as.numeric(Values))

COVID_cumsum_SC_by_county_3dayMA <- COVID_cumsum_SC_by_county %>% 
  group_by(County) %>% 
  mutate(Values_Lag = lag(Values, n = 3),
         Rolling_Avg_3_day = round((Values - Values_Lag)/3, 0),
         Rolling_Avg_3_day = if_else(is.na(Rolling_Avg_3_day), 0, Rolling_Avg_3_day))
```

Current Global Status
======================================================================

Row
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}
valueBox(value = paste(format(sum(df_details_country$cases), big.mark = ","), "", sep = " "), 
         caption = "Global: Confirmed Cases", 
         icon = "fas fa-user-md")
```

### death {.value-box}

```{r}
valueBox(value = paste(format(sum(df_details_country$deaths, na.rm = TRUE), big.mark = ","), " (",
                       round(100 * sum(df_details_country$deaths, na.rm = TRUE) / sum(df_details_country$cases), 1), 
                       "%)", sep = ""),
         caption = "Global: Death Rate", 
         icon = "fas fa-heartbeat", 
         color = death_color)
```

### tested {.value-box}

```{r}
valueBox(value = paste(format(df_details_USA_tested$tested, big.mark = ","), "", sep = " "), 
         caption = "USA: Total Tested",
         icon = "fas fa-ambulance")
```

### confirmed_USA {.value-box}

```{r}
valueBox(value = paste(format(df_details_USA_tested$cases, big.mark = ","), " (",
                       round(100 * df_details_USA_tested$cases / df_details_USA_tested$tested, 1), 
                       "%)", sep = ""), 
         caption = "USA: Confirmed Cases", 
         icon = "fas fa-user-md")
```

### death_USA {.value-box}

```{r}
valueBox(value = paste(format(df_details_USA_tested$deaths, big.mark = ","), " (",
                       round(100 * df_details_USA_tested$deaths / df_details_USA_tested$cases, 1), 
                       "%)", sep = ""),
         caption = "USA: Death Rate", 
         icon = "fas fa-heartbeat",
         color = death_color)
```

Row
-----------------------------------------------------------------------

### **Confirmed Cases of Coronavirus**

```{r}
plot_ly(COVID_cumsum_plotly_confirmed,
        x = ~Date,
        y = ~Confirmed,
        name = ~Country,
        type = "scatter",
        mode = "lines",
        hoverinfo = "text",
        text = ~paste("Country:", Country,
                      "

 Date:", Date,
                      "
 Confirmed:", format(Confirmed, big.mark = ","))) %>% 
  layout(yaxis = list(title = "Confirmed Cases"))
```

Row
-----------------------------------------------------------------------

### **Daily COVID-19 New Infected since exceeding Country's 100th Case**

```{r}
plot_ly(head(COVID_totals, n = 25),
        x = ~Infected_per_Day_since_100,
        y = ~reorder(Country, Infected_per_Day_since_100),
        type = "bar",
        marker = list(color = 'rgba(58, 71, 80, 0.50)',
                      line = list(color = 'rgba(58, 71, 80, 1.0)',
                                  width = 1)),
        hoverinfo = "text",
        text = ~paste("Infected:",
                      "

", format(Infected_per_Day_since_100, big.mark = ","))) %>% 
  layout(xaxis = list(title = "Infected per Day",
                      tickformat = ",d"),
         yaxis = list(title = "",
                      dtick = 1)) %>% 
  add_annotations(x = ~Infected_per_Day_since_100 + 380,
                  text = ~format(Infected_per_Day_since_100, big.mark = ","),
                  showarrow = FALSE)
```

### **Percent of Population Confirmed Infected with Coronavirus**

```{r}
plot_ly(head(COVID_totals, n = 25),
        x = ~Percent_Pop_Confirmed,
        y = ~reorder(Country, Percent_Pop_Confirmed),
        type = "bar",
        marker = list(color = 'rgba(58, 71, 80, 0.50)',
                      line = list(color = 'rgba(58, 71, 80, 1.0)',
                                  width = 1)),
        hoverinfo = "text",
        text = ~paste("% of Population Infected:",
                      "

", scales::percent(Percent_Pop_Confirmed))) %>% 
  layout(xaxis = list(title = "Percent of Population Infected",
                      tickformat = ".2%"),
         yaxis = list(title = "",
                      dtick = 1)) %>% 
  add_annotations(x = ~Percent_Pop_Confirmed + 0.00019,
                  text = ~scales::percent(Percent_Pop_Confirmed, accuracy = 0.001),
                  showarrow = FALSE)
```

### **Death Rate from Confirmed Infected with Coronavirus**

```{r}
plot_ly(head(COVID_totals, n = 25),
        x = ~Death_Rate,
        y = ~reorder(Country, Death_Rate),
        type = "bar",
        marker = list(color = 'rgba(58, 71, 80, 0.50)',
                      line = list(color = 'rgba(58, 71, 80, 1.0)',
                                  width = 1)),
        hoverinfo = "text",
        text = ~paste("Death Rate:",
                      "

", scales::percent(Death_Rate))) %>% 
  layout(xaxis = list(title = "Death Rate as % of Confirmed Infected",
                      tickformat = ".2%"),
         yaxis = list(title = "",
                      dtick = 1)) %>% 
  add_annotations(x = ~Death_Rate + 0.0075,
                  text = ~scales::percent(Death_Rate, accuracy = 0.01),
                  showarrow = FALSE)
```


Forecast (Global)
======================================================================

Row
-----------------------------------------------------------------------

### **Forecasted Cases of Coronavirus**

```{r}
plot_ly(data = COVID_cumsum_confirmed_hist_and_pred,
        x = ~Date,
        y = ~Values,
        name = ~Country,
        type = "scatter",
        mode = "lines",
        hoverinfo = "text",
        text = ~paste("Country:", Country,
                      "

 Date:", Date,
                      "
 Confirmed:", format(Values, big.mark = ","))) %>% 
  layout(yaxis = list(title = "Projected Cases"))
```

Row
-----------------------------------------------------------------------

### **Forecasted Coronavirus Deaths**

```{r}
plot_ly(data = COVID_cumsum_death_hist_and_pred,
        x = ~Date,
        y = ~Values,
        name = ~Country,
        type = "scatter",
        mode = "lines",
        hoverinfo = "text",
        text = ~paste("Country:", Country,
                      "

 Date:", Date,
                      "
 Confirmed:", format(Values, big.mark = ","))) %>% 
  layout(yaxis = list(title = "Projected Deaths"))
```

Forecast (US by State)
======================================================================

Row
-----------------------------------------------------------------------

### **Forecasted Cases of Coronavirus**

```{r}
plot_ly(data = COVID_cumsum_USA_confirmed_hist_and_pred,
        x = ~Date,
        y = ~Values,
        name = ~State,
        type = "scatter",
        mode = "lines",
        hoverinfo = "text",
        text = ~paste("State:", State,
                      "

 Date:", Date,
                      "
 Confirmed:", format(Values, big.mark = ","))) %>% 
  layout(yaxis = list(title = "Projected Cases"))
```

Rolling Averages (US & by State)
======================================================================

Row
-----------------------------------------------------------------------

### **3-day Rolling Average of new Coronavirus Cases by US State**

```{r}
plot_ly(COVID_cumsum_USA_confirmed_hist_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~State,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("State:", State,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

Row {.tabset}
-----------------------------------------------------------------------

### **California**

```{r}
plot_ly(COVID_cumsum_California_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

### **Colorado**

```{r}
plot_ly(COVID_cumsum_CO_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

### **Florida**

```{r}
plot_ly(COVID_cumsum_FL_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

### **Georgia**

```{r}
plot_ly(COVID_cumsum_GA_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

### **New York**

```{r}
plot_ly(COVID_cumsum_NY_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

### **Ohio**

```{r}
plot_ly(COVID_cumsum_OH_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

### **South Carolina**

```{r}
plot_ly(COVID_cumsum_SC_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

### **Texas**

```{r}
plot_ly(COVID_cumsum_TX_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

### **Washington, D.C.**

```{r}
plot_ly(COVID_cumsum_DC_by_county_3dayMA,
        x = ~Date,
        y = ~Rolling_Avg_3_day,
        name = ~County,
        type = "scatter",
        mode = "lines+markers",
        hoverinfo = "text",
        text = ~paste("County:", County,
                      "

 Date:", Date,
                      "
 New Cases Rolling Avg:", format(Rolling_Avg_3_day, big.mark = ","))) %>% 
  layout(yaxis = list(title = "3-day Rolling Average of new Coronavirus Cases"))
```

Map (Current US Status)
======================================================================

Row
-----------------------------------------------------------------------

### **Confirmed Cases by County**

```{r}
library(maps)

COVID_cumsum_USA_by_County_map <- COVID_cumsum_USA_by_County_map %>% 
  mutate(State_Name = as.factor(State),
         County_Name = as.factor(County),
         state_lower = tolower(State)) %>% 
  rename(county = County,
         state = State) %>% 
  select(-lat, -long)

COVID_cumsum_USA_by_County_map$county <- tolower(gsub(" County", "", COVID_cumsum_USA_by_County_map$county))
county_df <- map_data("county")
names(county_df) <- c("long", "lat", "group", "order", "state_lower", "county")

state_df <- map_data("state")

choropleth <- merge(county_df, COVID_cumsum_USA_by_County_map, by = c("state_lower", "county"))
choropleth <- choropleth[order(choropleth$order), ]
choropleth$Infected_Rate_Bucket <- cut(choropleth$Percent_of_County_Infected, breaks = c(seq(0, 0.002, by = 0.0005), 0.1))

# Use ggplot2 for base graph then wrap plotly interactivity to it
p <- ggplot(choropleth, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = Infected_Rate_Bucket), 
               colour = alpha("white", 1/2), size = 0.2) + 
  geom_polygon(data = state_df, colour = "white", fill = NA) +
  scale_fill_brewer(palette = "OrRd") + theme_void()

ggplotly(p)
```

About
======================================================================

### **About the COVID-19 Explorer**

This dashboard is a work in progress and meant as an exercise for R Markdown and flexdashboard. The forecasts are basic univariate projections that do not consider multiple variables in the COVID-19 crisis.

**Data soures:**

 - The primary data source is the [Corona Data Scraper](https://coronadatascraper.com)
 - Corona Data Scraper pulls COVID-19 Coronavirus case data from over 115 local and international verified sources, finds the corresponding GeoJSON features, and adds population data.
 - Inspiration for this dashboard came from Rami Krispin's [coronavirus](https://github.com/RamiKrispin/coronavirus) R package and GitHub page
 
**R Packages:**
 
 - tidyverse (dplyr, ggplot2, tidyr, stringr, lubridate, purrr)
 - maps
 - flexdashboard
 - wppExplorer
 - plotly
 - tsibble
 - fable
 - feasts
 
**Disclaimer:**
 
The views expressed on my GitHub repositories are mine alone and do not reflect the views of Javier Orraca's employer, Health Net, a Centene Corporation company.

**Additional Information:**

For data science career advice and tips, check out my blog, resources list, and Scatter Podcast, a data analytics podcast that I host, via my personal website at [https://www.javierorraca.com](https://www.javierorraca.com) or [Scatter Podcast](https://soundcloud.com/scatterpodcast).